forked from Archives/langchain
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Co-authored-by: Daniel Whitenack <whitenack.daniel@gmail.com>
1.7 KiB
1.7 KiB
Prediction Guard
This page covers how to use the Prediction Guard ecosystem within LangChain. It is broken into two parts: installation and setup, and then references to specific Prediction Guard wrappers.
Installation and Setup
- Install the Python SDK with
pip install predictionguard
- Get an Prediction Guard access token (as described here) and set it as an environment variable (
PREDICTIONGUARD_TOKEN
)
LLM Wrapper
There exists a Prediction Guard LLM wrapper, which you can access with
from langchain.llms import PredictionGuard
You can provide the name of your Prediction Guard "proxy" as an argument when initializing the LLM:
pgllm = PredictionGuard(name="your-text-gen-proxy")
Alternatively, you can use Prediction Guard's default proxy for SOTA LLMs:
pgllm = PredictionGuard(name="default-text-gen")
You can also provide your access token directly as an argument:
pgllm = PredictionGuard(name="default-text-gen", token="<your access token>")
Example usage
Basic usage of the LLM wrapper:
from langchain.llms import PredictionGuard
pgllm = PredictionGuard(name="default-text-gen")
pgllm("Tell me a joke")
Basic LLM Chaining with the Prediction Guard wrapper:
from langchain import PromptTemplate, LLMChain
from langchain.llms import PredictionGuard
template = """Question: {question}
Answer: Let's think step by step."""
prompt = PromptTemplate(template=template, input_variables=["question"])
llm_chain = LLMChain(prompt=prompt, llm=PredictionGuard(name="default-text-gen"), verbose=True)
question = "What NFL team won the Super Bowl in the year Justin Beiber was born?"
llm_chain.predict(question=question)